Showing papers in "Circuits Systems and Signal Processing in 2018"

Iterative Parameter Estimation for Signal Models Based on Measured Data

Abstract: This paper studies the modeling of multi-frequency signals based on measured data. With the use of the hierarchical identification principle and the iterative search, several iterative parameter estimation algorithms are derived for the signal models with the known frequencies and the unknown frequencies. For the multi-frequency signals, the hierarchical estimation algorithms are derived by means of parameter decomposition. Through the decomposition, the original optimization problem is transformed into the combination of the nonlinear optimization and the linear optimization problems. The simulation results show that the proposed hierarchical algorithms have better performance than the overall estimation algorithms without parameter decomposition.

Recursive and Iterative Least Squares Parameter Estimation Algorithms for Multiple-Input–Output-Error Systems with Autoregressive Noise

Abstract: This paper considers the parameter estimation of a multiple-input–output-error system with autoregressive noise. In order to solve the problem of the information vector containing unknown inner variables, an auxiliary model-based recursive generalized least squares algorithm and a least squares-based iterative algorithm are proposed according to the auxiliary model identification idea and the iterative search principle. The simulation results indicate that the least squares-based iterative algorithm can generate more accurate parameter estimates than the auxiliary model-based recursive generalized least squares algorithm. Two examples are given to test the proposed algorithms.

EEG-Based Biometric Authentication Using Gamma Band Power During Rest State

Abstract: Electroencephalography (EEG), one of the most effective noninvasive methods for recording brain’s electrical activity, has widely been employed in the diagnosis of brain diseases for a few decades. Recently, the promising biometric potential of EEG, for developing person identification and authentication systems, has also been explored. This paper presents the superior performance of power spectral density (PSD) features of gamma band (30–50 Hz) in biometric authentication, compared to delta, theta, alpha and beta band of EEG signals during rest state. The proposed authentication technique based on simple cross-correlation values of PSD features extracted from 19 EEG channels during eyes closed and eyes open rest state conditions among 109 subjects offers an equal error rate (EER) of 0.0196 which is better than the state-of-the-art method employing eigenvector centrality features extracted from gamma band of 64 EEG channels of the same dataset. The obtained results are promising, but further investigation is essential for exploring the subject-specific neural dynamics and stability of gamma waves and for optimizing the results.

A Fractional Gradient Descent-Based RBF Neural Network

Abstract: In this research, we propose a novel fractional gradient descent-based learning algorithm (FGD) for the radial basis function neural networks (RBF-NN). The proposed FGD is the convex combination of the conventional, and the modified Riemann–Liouville derivative-based fractional gradient descent methods. The proposed FGD method is analyzed for an optimal solution in a system identification problem, and a closed form Wiener solution of a least square problem is obtained. Using the FGD, the weight update rule for the proposed fractional RBF-NN (FRBF-NN) is derived. The proposed FRBF-NN method is shown to outperform the conventional RBF-NN on four major problems of estimation namely nonlinear system identification, pattern classification, time series prediction and function approximation.

Hyperchaotic Memcapacitor Oscillator with Infinite Equilibria and Coexisting Attractors

Abstract: A newly introduced charge-controlled memcapacitor-based hyperchaotic oscillator with coexisting chaotic attractors is investigated. Dynamic analysis of the oscillator shows that it has infinite number of equilibrium points and shows multistability. Its multistability analysis in the parameter space shows the existence of chaotic and hyperchaotic attractors. Fractional-order analysis of the hyperchaotic oscillator shows that the hyperchaos remains in the fractional order too. Field programmable gate arrays are used to realize the proposed oscillator.

Image Edge Detection Using Fractional Calculus with Feature and Contrast Enhancement

Abstract: Edge detection is an important aspect of image processing to improve image edge quality. In the literature, there exist various edge detection techniques in spatial and frequency domains that use integer-order differentiation operators. In this paper, we have implemented feature and contrast enhancement of image using Riemann–Liouville fractional differential operator. Based on the direction of strong edge, we have evaluated edge components and carried out a performance analysis based on several well-known metrics. We have also improved the pixel contrast based on foreground and background gray level. Moreover, by theoretical and experimental results, it is observed that the proposed feature and contrast enhancement outperforms the existing methods under comparison. We have discussed that the edge components calculated using fractional derivative can be used for texture and contrast enhancement. This paper is based on fractional-order differentiation operation to detect edges with the help of the directional edge components across eight directions. The experimental comparison results are shown in tabular form and as qualitative texture results. The six experimental input images are used to analyze various performance metrics. The experiments show that for any grayscale image the proposed method outperforms classical edge detection operators.

A Novel Fractional Gradient-Based Learning Algorithm for Recurrent Neural Networks

Abstract: In this research, we propose a novel algorithm for learning of the recurrent neural networks called as the fractional back-propagation through time (FBPTT) Considering the potential of the fractional calculus, we propose to use the fractional calculus-based gradient descent method to derive the FBPTT algorithm The proposed FBPTT method is shown to outperform the conventional back-propagation through time algorithm on three major problems of estimation namely nonlinear system identification, pattern classification and Mackey–Glass chaotic time series prediction

A Recursive Identification Algorithm for Wiener Nonlinear Systems with Linear State-Space Subsystem

Abstract: This paper addresses the problem of recursive identification of Wiener nonlinear systems whose linear subsystems are observable state-space models. The maximum likelihood principle and the recursive identification technique are employed to develop a recursive maximum likelihood identification algorithm which estimates the unknown parameters and the system states interactively. In comparison with the developed recursive maximum likelihood algorithm, a recursive generalized least squares algorithm is also proposed for identification of such Wiener systems. The performance of the developed algorithms is validated by two illustrative examples.

Automatic Radar Waveform Recognition Based on Deep Convolutional Denoising Auto-encoders

Abstract: Aimed at the deficiency of traditional feature extraction techniques in radar emitter recognition, a novel deep feature extraction and recognition architecture is proposed. To fit into the model, the time-domain emitters are transformed into unique time-frequency images correspondingly. Since auto-encoders restrict the input data to be vector-form and convolutional model is hard to optimize, denoising auto-coders are stacked in a convolutional manner in the pre-training stage and the proposed framework is trained by greedy layer-wise algorithm. The optimized network parameters are employed to initialize convolutional neural networks. By layers of mapping and pooling, deep time-frequency features are extracted, which are fed into the collaborative representation-based classifier to implement classification task. Experimental results on simulated data validate the feasibility of the proposed architecture. Furthermore, compared with conventional shallow algorithms, the proposed one can obtain higher recognition accuracy and more robust performance. Taking advantage of collaborative representation, the proposed algorithm is more applicable to the small-sample-size case.

Effect of Different Approximation Techniques on Fractional-Order KHN Filter Design

Abstract: Having an approximate realization of the fractance device is an essential part of fractional-order filter design and implementation. This encouraged researchers to introduce many approximation techniques of fractional-order elements. In this paper, the fractional-order KHN low-pass and high-pass filters are investigated based on four different approximation techniques: Continued Fraction Expansion, Matsuda, Oustaloup, and Valsa. Fractional-order filter fundamentals are reviewed then a comparison is made between the ideal and actual characteristic of the filter realized with each approximation. Moreover, stability analysis and pole movement of the filter with respect to the transfer function parameters using the exact and approximated realizations are also investigated. Different MATLAB numerical simulations, as well as SPICE circuit results, have been introduced to validate the theoretical discussions. Also, to discuss the sensitivity of the responses to component tolerances, Monte Carlo simulations are carried out and the worst cases are summarized which show good immunity to component deviations. Finally, the KHN filter is tested experimentally.

Filtering-Based Maximum Likelihood Gradient Iterative Estimation Algorithm for Bilinear Systems with Autoregressive Moving Average Noise

Abstract: This paper combines the maximum likelihood principle with the data filtering technique for parameter estimation of bilinear systems with autoregressive moving average noise. We give the input–output representation of the bilinear systems through eliminating the state variables in the model. Based on the obtained model, we use an estimated noise transfer function to filter the input–output data and derive a filtering-based maximum likelihood gradient iterative algorithm for identifying the parameters of bilinear systems with colored noises. A gradient-based iterative algorithm is given for comparison. The simulation results indicate that the proposed algorithm is effective for identifying bilinear systems.

Bistable Hidden Attractors in a Novel Chaotic System with Hyperbolic Sine Equilibrium

Abstract: For the past 4 years, there has been a rapid rise in the study of chaotic systems with curves of equilibria which are categorized as systems with hidden attractors. There is still significant controversy surrounding the shapes of equilibrium points. This paper presents a new three-dimensional autonomous chaotic system with hyperbolic sine equilibrium. Fundamental dynamical properties and complex dynamics of the system have been discovered by using equilibrium analysis, phase portrait, Poincare map, bifurcation diagram and Lyapunov spectrum. It is crucial to note that there are bistable hidden chaotic attractors in the introduced system. Furthermore, in order to show the feasibility of the new system with hyperbolic sine equilibrium, its electronic circuit has been implemented.

A 0.3-V Pseudo-Differential Bulk-Input OTA for Low-Frequency Applications

Abstract: This paper presents an ultra-low-voltage high-performance bulk-input pseudo-differential operational transconductance amplifier for low-frequency applications. The proposed amplifier is designed using standard 65-nm CMOS technology and powered from 0.3-V supply with a stand by current consumption of 170-nA. Post-layout simulations with a load capacitance of 5 pF have been performed to validate the performance of the proposed amplifier. The proposed amplifier exhibits a DC gain of 60 dB and a phase margin of 53 $$^\circ $$ at unity gain frequency of 70 kHz for a load of 5 pF. The proposed OTA has shown improvement of five times and more than 2.5 times in small-signal and large-signal performance, respectively, when compared to the state of the art. In addition, the proposed transconductance amplifier is used to design a tunable second-order $$G_m{\text {-}}C$$ low-pass filter. Simulation results show that tunable cutoff frequency is varying from 4 to 190 kHz which is obtained by varying the input-stage bias current from 1 to 200 nA.

A Modified Fractional-Order Unscented Kalman Filter for Nonlinear Fractional-Order Systems

Abstract: In this paper, a fractional-order unscented Kalman filter (FUKF) is introduced at first. Then, its convergence is analyzed based on Lyapunov functions for nonlinear fractional-order systems. Specific conditions are obtained that guarantee the boundedness of the FUKF estimation error. In addition, an adaptive noise covariance is suggested to overcome huge estimation errors. Since the adaptation law plays a crucial role in the performance of the proposed method, a fuzzy logic based method is also presented to improve the adaptive noise covariance. Therefore, a modified FUKF is proposed to increase the convergence and the accuracy of the estimation. Finally, the proposed algorithm is implemented to estimate the states of a two electric pendulum system and its performance is analyzed. Simulation results show that a huge estimation error leads to the FUKF divergence; however, the modified fractional-order unscented Kalman filter with fuzzy performs an accurate state estimation.

Hyperchaotic Attractor in a Novel Hyperjerk System with Two Nonlinearities

Abstract: Hyperjerk systems have received considerable interest in the literature because of their simplicity and complex dynamical properties In this work, we introduce a novel hyperjerk system with an absolute nonlinearity and a quintic term Interestingly, the hyperjerk system exhibits hyperchaotic behavior Dynamics and the feasibility of the hyperjerk system are discovered by using numerical analysis and circuit implementation Moreover, adaptive controllers have been designed for stabilization and synchronization of the new hyperjerk system The control results have been established by using Lyapunov stability theory, and numerical simulations with MATLAB have been shown to illustrate the validity of the constructed adaptive controllers

Novel CMOS Dual-X Current Conveyor Transconductance Amplifier Realization with Current-Mode Multifunction Filter and Quadrature Oscillator

Abstract: This paper presents a novel active element, namely dual-X current conveyor transconductance amplifier. The CMOS implementation, parasitic model and characteristic performance parameters of the proposed dual-X current conveyor transconductance amplifier have been explored. The proposed active element has the advantages of good operational bandwidth, good dynamic range and low power consumption. Additionally, current-mode multifunction filter and quadrature oscillator are proposed to examine the applicability of the newly proposed active element. The proposed current-mode multifunction filter provides the responses, low-pass, high-pass and band-pass simultaneously without any circuit modification. The proposed quadrature oscillator circuit simultaneously generates three current outputs and three voltage outputs. The non-ideal analyses of both current-mode multifunction filter and quadrature oscillator are also included. Moreover, the active and passive sensitivities of both current-mode multifunction filter and quadrature oscillator are calculated which are found to be less than unity in magnitude. HSPICE simulation results are depicted to confirm the theoretical analyses. Moreover, practicality of novel dual-X current conveyor transconductance amplifier is examined through the experimental results of the proposed quadrature oscillator.

Fast and Stable Computation of the Charlier Moments and Their Inverses Using Digital Filters and Image Block Representation

Abstract: In this paper, we suggest a new method of fast and stable calculation of the discrete orthogonal moments of Charlier and their inverses. This method is meant to accelerate the computation time and improve the quality of images reconstruction. In this method, we have combined two main concepts. The first concept is the digital filters based on the Z-transform to accelerate the calculation process of the discrete orthogonal moments of Charlier. The second concept is the partitioning of the image into a set of blocks of fixed sizes where each block is processed independently. The significant reduction in the image space during partitioning makes it possible to represent the minute details of the image with only low orders of Charlier’s discrete orthogonal moments, which ensures the digital stability during the processing of the image. In order to demonstrate the efficiency, stability, and precision of our method compared to other existing methods, some simulations have been performed on different types of binary images and gray images with and without noise.

On the Analysis and Design of Fractional-Order Chebyshev Complex Filter

Abstract: This paper introduces the concept of fractional-order complex Chebyshev filter. A fractional variation of Chebyshev differential equations is introduced based on Caputo fractional operator. The proposed equation is solved using fractional Taylor power series method. The condition for fractional polynomial solutions is obtained and the first four polynomials scaled using an appropriate scaling factor. The fractional-order complex Chebyshev low-pass filter based on the obtained fractional polynomials is developed. Two methods for obtaining the transfer functions of the complex filter are discussed. Circuit implementations are simulated using Advanced Design System (ADS) and compared with MATLAB numerical simulation of the obtained transfer functions to prove the validity of the two approaches.

Adaptive Fuzzy Observer-Based Fault-Tolerant Control for Takagi–Sugeno Descriptor Nonlinear Systems with Time Delay

Abstract: This paper investigates the problems of state/fault estimation and active fault-tolerant control (AFTC) design for time-delay descriptor fuzzy systems subject to external disturbances and actuator faults. Using Takagi–Sugeno fuzzy models, an adaptive fuzzy observer is proposed to achieve system state and actuator fault estimation simultaneously. According to Lyapunov functional method, design and analysis conditions of the resulting closed-loop delayed descriptor system are formulated in terms of linear matrices inequalities (LMIs). Observer and controller gains are computed by solving a set of LMIs in only one step and then used to both estimate the unmeasured states and actuator faults in AFTC context. Numerical examples are provided to show the merit and the conservativeness of the proposed approach in comparison with the existing methods by considering various types of actuator faults.

An Efficient ECG Denoising Technique Based on Non-local Means Estimation and Modified Empirical Mode Decomposition

Abstract: Noninvasive nature of Electrocardiogram (ECG) signal makes it widely accepted for cardiac diagnosis. During the process of data acquisition, ECG signal is generally corrupted by a number of noises. Further, during ambulatory monitoring and wireless recording, ECG signal gets corrupted by additive white Gaussian noise. Without affecting the morphological structure, denoising of ECG signal is essential for proper diagnosis. This paper presents an ECG denoising method based on an effective combination of non-local means (NLM) estimation and empirical mode decomposition (EMD). Earlier works have shown that the patch-based NLM approach is insufficient for denoising the under-averaged region near high-amplitude QRS complex. To address this issue, the denoised signal obtained by NLM is decomposed into intrinsic mode functions (IMFs) using EMD in this work. Next, thresholding of the IMFs is done using the instantaneous half period criterion and the soft-thresholding to obtain the final denoised output. Furthermore, the modified empirical mode decomposition (M-EMD) is used in the place of standard EMD to reduce the computational cost. Performance of the proposed method is tested on a number of ECG signals from the MIT-BIH database. The experimental results presented in this paper show that the aforementioned shortcoming of the NLM method is addressed to a large extent. Moreover, the proposed approach provides improved performance when compared to different state-of-the-art ECG denoising methods.

Review and Analysis of Evolutionary Optimization-Based Techniques for FIR Filter Design

Abstract: Recent times have witnessed a wide application of evolutionary optimization by researchers, in design of digital FIR filters, based on frequency domain specifications. A significant growth has been reported in the field of evolutionary optimization-based FIR filter design. Optimization-based techniques are used to solve the filter design problem by framing the design task as an error function which is further solved to determine the filter coefficients that satisfies the desired specifications. However, the nonlinear, non-differentiable, non-convex, multimodal nature of the associated optimization problem makes the design task quite challenging. In this regard, a number of evolutionary optimization- based techniques have been applied for FIR filter design. This paper provides a comprehensive review of the various evolutionary optimization-based techniques for FIR filter design. In addition to the review, the reported techniques have been analyzed by implementing them on a common platform and comparing them in terms of their effectiveness in meeting the desired specifications.

Sparsity-Aware Adaptive Directional Time–Frequency Distribution for Source Localization

Abstract: Multi-component characteristics and missing data samples introduce artifacts and cross-terms in quadratic time–frequency distributions, thus affecting their readability. In this study, we propose a new time–frequency method that employs directional smoothing and compressive sensing to reduce cross-terms and mitigate artifacts associated with missing samples. The efficacy of the proposed time–frequency distribution for solving real-life problems is illustrated by employing it to estimate direction of arrival of sparsely sampled sources in under-determined scenario. Numerical results show that the proposed method is superior to other state-of-the-art methods both in terms of obtaining clear time–frequency representation and accurately estimating direction of arrival.

Locally Optimized Adaptive Directional Time–Frequency Distributions

Abstract: This paper addresses the problem of estimating the parameters of adaptive directional time–frequency distributions (ADTFDs). ADTFDs locally optimize the direction of the smoothing kernel on the basis of directional Gaussian or double derivative directional Gaussian filter. Conventionally, the parameters of these techniques have to be tuned manually for each particular signal. Global optimization of the parameters fails to provide the desired results when the signal encompasses close or short-duration components. We propose a two-stage algorithm to resolve this issue. As part of the first stage, the length of the smoothing kernel is optimized globally. In the second stage, the parameters which control the shape of the selected smoothing window are optimized, locally. It is shown that the multistage algorithm can result in a time–frequency distribution that has both high resolution for close components and good concentration of signal energy for short-duration signal components. Experimental findings reveal the superiority of the proposed technique over the existing methods in the case of complete signals and its benefits in the case of signals with missing samples.

Huber-Based Adaptive Unscented Kalman Filter with Non-Gaussian Measurement Noise

Abstract: This paper concerns the application of Huber-based robust unscented Kalman filter (HRUKF) in nonlinear system with non-Gaussian measurement noise. The tuning factor $$\gamma $$ is key factor in determining the form of Huber cost function. Traditionally, $$\gamma $$ is mainly determined by experience and/or experiments. It is hard to acquire optimal parameter or achieve an optimal filtering. To solve this problem, the influence of tuning factor $$\gamma $$ on the performance of HRUKF is analyzed, and then, an adaptive strategy based on projection statistics algorithm for this parameter is proposed to improve filtering performance under the conditions that the measurement noise is contaminated by heavier tails and/or outliers. Simulation results for the problem of Reentry Vehicle Tracking demonstrate the superiority of the proposed method over the traditional ones.

Eigenvalue Decomposition of Hankel Matrix-Based Time-Frequency Representation for Complex Signals

Abstract: The analysis of non-stationary signals using time-frequency representation (TFR) presents simultaneous information in time and frequency domain. Most of TFR methods are developed for real-valued signals. In several fields of science and technology, the study of unique information presented in the complex form of signals is required. Therefore, an eigenvalue decomposition of Hankel matrix-based TFR method, which is a data-driven technique, has been extended for the analysis of complex-valued signals. In this method, the positive and negative frequency components of complex signals are separately decomposed using recently developed eigenvalue decomposition of Hankel matrix-based method. Further, the Hilbert transform is applied on decomposed components to obtain TFR for both positive and negative frequency ranges. The proposed method for obtaining TFR is compared with the existing methods. Results for synthetic and natural complex signals provide support to the proposed method to perform better than compared methods.

Design of High-Performance ECG Detector for Implantable Cardiac Pacemaker Systems using Biorthogonal Wavelet Transform

Abstract: A digital electrocardiogram (ECG) detector with low power consumption and high performance based on biorthogonal 2.2 wavelet transform and applicable for the modern implantable cardiac pacemakers is proposed in the present work. Biorthogonal 2.2 wavelet transform is chosen due to its high SNR, less number of coefficients, resemblance of shape with ECG wave and ability to increase QRS complex detection performance. Architecture of the proposed ECG detector includes modified biorthogonal 2.2 wavelet filter bank and a modified soft threshold-based QRS complex detector. Three low-pass filters and one high-pass filter with pipelined architecture are used which are lesser than the earlier designed detectors. Various blocks of proposed detector are designed to denoise the input ECG signal and then to find the correct location of R-wave. Verilog hardware description language for design entry, Modelsim embedded in Xilinx ISE v.14.1 for simulation, Virtex-6 FPGAs for synthesis and Xilinx ISE tools are used to measure the performance, area and power of the proposed ECG detector and its constituent blocks. A low detection error rate of 0.13%, positive predictivity ( $$\hbox {P}^{+}$$ ) of 99.94% and sensitivity ( $$\hbox {S}_{\mathrm{e}}$$ ) of 99.92% are achieved for the proposed ECG detector which are better compared to the previous results. Also, it consumes only 20 mW of total power at 50 KHz and shows the overall delay of 18.924 ns which makes it useful for the low power and high-performance applications.

VLSI-Based Pipeline Architecture for Reversible Image Watermarking by Difference Expansion with High-Level Synthesis Approach

Abstract: The paper focuses on the VLSI-based digital design and implementation of reversible image watermarking (RIW) architecture using difference expansion (DE). Mathematical simplicity of using a set of linear transformations leads to the choice of DE-based technique for developing hardware design. Moreover, its high performance gain in terms of payload capacity and the visual quality of the watermarked images would make this hardware architecture useful for real-time application on security purpose of medical and military images. High-level synthesis approach with resource-constraint design makes the architecture novel that needs only single adder, subtractor, multiplier, and divider along with 20 registers and 14 multiplexers for embedding. The number of resource required is same for watermark decoding with a modified schedule, which is the specialty of this design. The results obtained after implementation of the architecture on Xilinx Virtex-7 Field Programmable Gate Array (FPGA), Zynq-7000 programmable System-on-Chip (SoC) show the viability of low cost, high speed and real-time use. To process an image block $$(8 \times 8)$$ , the latency is 226.733 ns for 150 MHz clock with throughput 35.284 Mbps and the critical path for single cycle is 5.674 ns. The obtained structural similarity (SSIM) performance quality metric of the RIW algorithm from MATLAB simulation is compared with the SSIM obtained from hardware, and excellent agreements between them are observed.

Robust Color Image Hashing Using Quaternion Polar Complex Exponential Transform for Image Authentication

Abstract: Image hashing is one of the multimedia protection techniques. In this paper, a new method for robust image hashing based on quaternion polar complex exponential transform (QPCET) is proposed. The proposed method targets two goals. The first goal is the robustness against geometric and common signal processing attacks. The second one is authenticating color images without conversion which keeps their color information. In the proposed method, the input color image is normalized by the bicubic interpolation and then the interpolated image passes to Gaussian low-pass filter. QPCET moments are used to extract features. Finally, the hash value is calculated using the extracted features. On the sender side, a secret key is utilized to increase the protection of the hash value before transmitting it. The hash value is attached with the transmitted color image. On the receiver side, the authenticity of the received image is checked by decrypting the hash value. Euclidean distance is used to check the similarity between different hashes. Results of the conducted experiments prove the robustness of proposed hash against different geometric and signal processing attacks. Also, it preserves the content of the transmitted color image. Hashing different images has a very low collision probability which ensure the suitability of the proposed method for image authentication. Comparison with the existing methods ensures the superiority of the proposed method.

Global Exponential Convergence of Fuzzy Cellular Neural Networks with Leakage Delays, Distributed Delays and Proportional Delays

Abstract: In this paper, a class of fuzzy cellular neural networks (FCNNs) with leakage delays, distributed delays and proportional delays are considered. Using the differential inequality strategy, some sufficient criteria which ensure the global exponential convergence of the FCNNs with leakage delays, distributed delays and proportional delays are established. Numerical simulations are given to explain the obtained analytical results. The derived conclusions of this article are new and complement some earlier publications.

Design and Analysis of Power Efficient Binary Content Addressable Memory (PEBCAM) Core Cells

Abstract: This paper presents the design and the analysis of power efficient binary content addressable memory (PEBCAM) core cells using the energy recovery principle of adiabatic logic. Generally, in the design of adiabatic CAM, the storage array is built by using a basic CAM cell, but the peripheral circuits are realized by using different adiabatic logic structures. In this paper, we propose the design of 3 novel power efficient binary content addressable memory core cells (PEBCAM core cells) using adiabatic logic, namely improved efficient charge recovery logic (IECRL) CAM core cell, positive feedback adiabatic logic (PFAL) CAM core cell and pass transistor adiabatic logic (PAL) CAM core cell. Memory arrays of size 4 $$\times $$? 4 were designed and implemented using the proposed PEBCAM core cells in 45nm CMOS technology. It was found that recovery of dissipated power using adiabatic logic was better than the other CAM structures. The simulation results of the PEBCAM-IECRL CAM proved to be better with a power saving of 77.8% than the conventional adiabatic CAM structures. The circuits were designed using 45nm CMOS technology with a sinusoidal power clock of 1 V and other node voltages at 0.7 V using Cadence Virtuoso.